A decade ago, we argued about whether artificial intelligence would replace writers and editors. Today the sharper question is simpler: can citizens still tell what is true? In India’s polyglot, always online public sphere, AI sits at the intersection of culture, content, literature and corporate communication, bending language, memory and trust.
Consider the 2024 general elections. Candidates and consultants used AI voice clones to speak in multiple dialects at once, stitching themselves into the intimacy of vernacular WhatsApp groups. The same toolbox also made it trivially easy to seed doctored videos and voice notes that travelled faster than any rebuttal. AI widened access to speech while narrowing the space for shared truth - a paradox that will define the next decade of Indian communication.
If you work in media or corporate communications, you can feel the ground shifting. The job is no longer “produce more content.” It is to operate credibility infrastructure: provenance, disclosure and rapid verification. That is not a futuretense wish list; it is the price of participating in India’s attention economy now.
Three collisions define this new interface of AI and culture.
The first is language as software. India’s most precious cultural asset is linguistic diversity, but AI treats language like code—compilable, translatable and re-mixable. In elections it enabled hyperpersonalised outreach at industrial scale; in boardrooms it lets brands test twenty versions of a slogan before lunch. That is liberating and destabilising at once. When a celebrity’s face or a leader’s voice can be conjured convincingly on demand, authorship becomes contested. Literature has always borrowed from collective memory; generative systems borrow at industrial scale. The responsibility to cite sources, respect consent and disclose synthesis moves from the poet’s ethics to the platform’s defaults.
The second is the rise of deepfakes. The Indian state has recognised the risk. Under the IT Rules, platforms have been reminded of obligations to act against manipulated media. During the 2024 cycle, newsrooms and brands learnt that even a short-lived fake can do long-lived damage—seeded at midnight, debunked by noon, and archived forever in someone’s phone as a residue of doubt. Police complaints over celebrity deepfakes and campaigntime fabrications were not outliers but previews of a standing risk to reputation and democracy.
The third is provenance as culture. Provenance used to be an artworld word. It is now the heart of digital culture. Open standards like Content Credentials embed tamper-evident trails about who made a photo or video, with which tools, and what edits were applied. Once niche, this is gaining Indian momentum: mainstream publishers have joined transparency coalitions, and infrastructure providers are beginning to preserve credentials at internet scale. Instead of telling audiences to trust your official handle, you ship verifiable context with the asset itself.
Inside companies, generative AI is already a junior writer, translator and analyst. Used well, it compresses the distance between insight and expression; used lazily, it produces generic sludge and, worse, confident untruths. In my ebook on corporate communications and CSR I argued that AI belongs in the workflow—but with human oversight, ethical prompts and rigorous verification. That advice ages well. The communicator’s edge is not speed; it is judgment.
The regulatory and self-regulatory landscape is evolving. India’s data protection law finally gives citizens a rights vocabulary, but does not yet spell out protections around automated decisionmaking. Advertising’s selfregulator has moved faster: influencer disclosures are now standard, and virtual influencers must state they are not human. Sectorspecific advisories on deepfakes round out a patchwork of obligations that boil down to the same ethic: label what’s synthetic, protect what’s personal, and remove what harms.
For newsrooms, brands and universities that shape culture, a culturefirst AI playbook has become essential. Label the machine—if any part of an artefact was AIgenerated, disclose it clearly and upfront. Adopt content credentials so provenance rides with your media from creation to syndication. Publish a synthetic media policy that spells out what you will never do and what you might attempt with consent and labelling. Run red team drills to prepare for weekend misinformation storms. And measure credibility, not just clicks—building scorecards that track provenance, speed of labelling, correction reach and employee literacy.
Writers have always used tools—from typewriters to grammar checkers. Generative AI is another instrument, not a muse. The danger is not that machines will write better poetry; it is that we normalise voice without accountability. Literature is a covenant between a reader and a voice. When that voice is trained on everything and responsible for nothing, the covenant frays. The way forward is not purism but provenance: use AI to draft, yes, but disclose; quote datasets carefully; let the human author carry the moral weight of the words.
Where India can lead is by establishing norms before the damage calcifies. Public broadcasters, leading newspapers and top advertisers could converge on a provenance baseline, turning content credentials into table stakes for professional media. Industry bodies could maintain a live registry of virtual influencers and synthetic spokespeople. The government should support open source verification tools in Indian languages for schools and district information officers.
We arrived at AI through code. We will live with it through culture. The institutions that will thrive are not those that post the most, but those that build and defend trust in plain sight. Authenticity now has to be engineered, and then performed, every day.
(Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the publication.)
